Visualizing and predicting evolution by mapping the elusive 'fitness landscape'

Suppose you were trying to design a vaccine to combat next season's influenza virus. Having a detailed map that tells you exactly how various strains of the flu bug will evolve would be extremely helpful.

Suppose you were trying to design a vaccine to combat next season's influenza virus. Having a detailed map that tells you exactly how various strains of the flu bug will evolve would be extremely helpful.

Creating just that sort of map is the goal of evolutionary biologists who study a conceptual tool called the fitness landscape, which provides a way to visualize and predict evolution.

The idea of the fitness landscape has been around since the 1930s, when it was proposed by population geneticist Sewall Wright. But mapping a detailed fitness landscape is a daunting challenge, and the landscapes created to date are fairly crude.

Now, a team of University of Michigan researchers is reporting the first comprehensive in vivo fitness landscape of a gene—roughly 100 times larger than any previous effort. The findings are expected to be of interest to evolutionary biologists, geneticists and molecular biologists, said team leader Jianzhi "George" Zhang, a professor in the U-M Department of Ecology and Evolutionary Biology.

A paper summarizing the team's findings, which are based on the manipulation of a single gene in baker's yeast, was published online in the journal Science on April 14, 2016

"The concept of the fitness landscape is critically important and underlies many evolutionary theories. But until now, we were simply not able to measure it," Zhang said. "We still have a long way to go, but this is a big step toward measuring fitness landscapes."

The other authors of the Science paper, "The fitness landscape of a tRNA gene," are U-M graduate students Chuan Li and Wenfeng Qian (now at the Chinese Academy of Sciences) and postdoctoral fellow Calum Maclean.